You wrote a lot of text but missed the central point. Cameras that can capture dark skin exist. This is a problem that human researchers just shrugged off or ignored. You might say well, can't do anything about it, our training corpus is full of these bad images. Maybe we blame the camera manufacturers?

But that's still a cop out. You can't use models with these problems. It gets you disasters like face unlock that doesn't work for black people. This happens even if people aren't white. Samsung famously released blink detection that didn't work on a lot of East Asian faces. The products are broken because the white bias is the default and pervades everyone's thinking.

What I am saying --which is absolutely true-- is that there's a lot more data in the mid-range of a gamma-encoded image (which is 100% of the images produced by everyone doing this work) than the low-lights. This means that the local dynamic range in those regions is different. Which means that operations such as edge detection will be more accurate and could make the difference between something working and not.

Vision researchers should take a class or two in cinematography and photography, it would serve them well. Even the quality of the lens makes a difference. Most work I've seen out there uses cameras that barely pass as security cameras or webcams.

That said, yes, ML needs to work with crappy images and every single camera out there. My argument is that you are not going to be able to train using crap data. And the images in a data set would be crap if the data --the images-- were not acquired using cameras and techniques that provide enough data across various segments of the dynamic range.

Again, I gave the example of my black GSD for a reason. You are not going to be able to recognize him as anything other than a blob on a couch without a camera that can capture enough data at the low end of the dynamic range and a system trained with that data.

The fact that Samsung (or anyone else) failed means nothing. In order for that data point to be meaningful you'd have to have intimate knowledge of what they were doing and what capabilities they had, both in terms of science and engineering as well as the consumer hardware they developed.

I have competed against multi-billion dollar multinational corporations who, despite their financial prowess and scale, could not design their way out of a paper bag. They don't understand the problem, lack creativity and, most importantly, absolutely lack the passion necessary to solve it. Ten 9-to-5 engineers can't compete with a single engineer passionate enough to devote every waking hour to solving difficult problems. It doesn't matter how much money you throw at them, they just can't perform.

> Cameras that can capture dark skin exist

This doesn’t help when the variance in feature space of white people’s faces is objectively a lot wider than the variance of black people’s faces. You can have the best camera in the world, but it’s not going to change the fact that someone from Ireland looks way more different versus someone from Italy (in any mechanical basis) than someone from Cameroon looks versus someone from Malawi.

Objectively? You have to show some evidence, otherwise this just sounds just like "yall look alike to me".

To add to this considering that genetic variations between peoples on the African continent are larger than the variations to anywhere else, this is also very unlikely. I think it's a reasonable assumption that genetic variation and variation in physical features are somewhat correlated.

HN has been seeing an increase in these types of posts lately.

Yes, I wonder if this is because in the political climate in the US some people feel more empowered for statements like this or if the number of people holding these believes is actually increasing (at HN)

I think GP may be reacting based on common techniques of obscuring faces - i.e. ninjas wearing black masks, special ops putting dark paint on their face to obscure details. I think the effectiveness of those techniques leads people to think that darker colors are harder to see or make out. But that analogy breaks down because here we're talking about fully lit situations, right?

> genetic variations between peoples on the African continent are larger than the variations to anywhere else

You are misinterpreting this, as does almost everyone else. Phenotypic variance is almost completely orthogonal to number of unique SNPs (which is what this generally refers to).

> genetic variation and variation in physical features are somewhat correlated.

This is not the case. You can have narrow population bottlenecks (reducing SNP diversity) followed by high variance in selective pressure. This is exactly what happened to early Europeans. You had a small initial population spread out to inhabit a large variety of ecosystems.

This is a farcical argument. Facial recognition apps aren’t looking to distinguish gross features like Irish vs Italian features (whatever those are). They can tell two brothers from Ireland apart from one another, they should be able to tell two brothers from Malawi apart too. Their mother probably can, so why the hell shouldn’t their phone?

Is this really the case though? Feature wise it's not just the color of the skin that matters, and populations across Africa can look very different from each other's even to the human eye.

The first mistake is to believe the definition of "bias" requires intent to harm. It doesn't. Any systemic error is "bias", even if it's just your thermometer always reporting 2 deg more than the actual temperature.

The second issue is believing some technical arcana are a valid excuse for selling products with such errors. It isn't! If your product reinforces entrenched discrimination, you either fix it or stop selling it.

Think of this from the perspective of the end consumer of the model: am I willing to buy new high-quality capture hardware for the target production environment, potentially discarding an existing investment?

In my experience, they rarely will even consider it.

Therefore, the training data should have the same technical shortcomings as what will be used in production.

> Therefore, the training data should have the same technical shortcomings as what will be used in production.

I think training and the application of the trained system are separable. Nothing is accomplished by training with data sets that lack data or detail. Inference or classification is impossible or deeply impaired by the lack of data.

As a hypothesis, the solution is likely to involve training one network with good data and a separate network to be the interface between the first network and the imperfect perceptual data in real-world applications.

At the end of the day AI/ML need to leave the world of classification behind and move on to the concept of understanding. This is not an easy task, yet it is necessary in order for these amazing technologies to truly become generally useful.

We don't show a human child ten thousand images of a thousand different cups in ten different orientations in order for that child to recognize and understand cups. The reason for this is that our brains evolved to give us the ability to understand, not simply classify. This means we need far fewer samples in order to have effective interactions with our physical world.

The focus on using massive data sets to train classification engines is a neat parlor trick, yet it will never result in understanding and is unlikely to develop into useful general artificial intelligence. The problem quickly becomes exponential and low quality data becomes noise. We need to develop paradigms for encoding understanding rather than massive classification networks that can't even match the performance of a dog in some applications. As I said before, this is a very difficult problem. I don't think we know how to do this yet. Not even sure we have any idea how to do it. I certainly don't.